-
Notifications
You must be signed in to change notification settings - Fork 0
/
simple_network.py
42 lines (31 loc) · 1.26 KB
/
simple_network.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 26 16:46:14 2016
@author: Nitin Bansal
Working on MNIST dataset for Classification of Digits
Accuracy obtained up to 98%
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
with tf.Session() as sess:
x = tf.placeholder("float",shape =[None,784])
y_ = tf.placeholder("float", shape = [None,10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(cross_entropy)
init_op = tf.initialize_all_variables()
init_op.run()
for i in range(1000):
batch_x,batch_y = mnist.train.next_batch(100)
train_step.run(feed_dict={x: batch_x, y_: batch_y})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
print (accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))